Sentence Similarity
sentence-transformers
Safetensors
Transformers
Dutch
bert
text-generation
text-embeddings-inference
Instructions to use clips/e5-base-v2-t2t with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use clips/e5-base-v2-t2t with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("clips/e5-base-v2-t2t") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use clips/e5-base-v2-t2t with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("clips/e5-base-v2-t2t") model = AutoModelForCausalLM.from_pretrained("clips/e5-base-v2-t2t", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add new SentenceTransformer model (#1)
Verified signature- Add new SentenceTransformer model (f172a843a051c7c33719450237039a6814abcafb) Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>a2911e1
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README.md updated
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